-
-
Notifications
You must be signed in to change notification settings - Fork 55.7k
/
test_stitching.py
181 lines (127 loc) · 6.37 KB
/
test_stitching.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
#!/usr/bin/env python
import cv2 as cv
import numpy as np
from tests_common import NewOpenCVTests
class stitching_test(NewOpenCVTests):
def test_simple(self):
img1 = self.get_sample('stitching/a1.png')
img2 = self.get_sample('stitching/a2.png')
stitcher = cv.Stitcher.create(cv.Stitcher_PANORAMA)
(_result, pano) = stitcher.stitch((img1, img2))
#cv.imshow("pano", pano)
#cv.waitKey()
self.assertAlmostEqual(pano.shape[0], 685, delta=100, msg="rows: %r" % list(pano.shape))
self.assertAlmostEqual(pano.shape[1], 1025, delta=100, msg="cols: %r" % list(pano.shape))
class stitching_detail_test(NewOpenCVTests):
def test_simple(self):
img = self.get_sample('stitching/a1.png')
finder= cv.ORB.create()
imgFea = cv.detail.computeImageFeatures2(finder,img)
self.assertIsNotNone(imgFea)
# Added Test for PR #21180
self.assertIsNotNone(imgFea.keypoints)
matcher = cv.detail_BestOf2NearestMatcher(False, 0.3)
self.assertIsNotNone(matcher)
matcher = cv.detail_AffineBestOf2NearestMatcher(False, False, 0.3)
self.assertIsNotNone(matcher)
matcher = cv.detail_BestOf2NearestRangeMatcher(2, False, 0.3)
self.assertIsNotNone(matcher)
estimator = cv.detail_AffineBasedEstimator()
self.assertIsNotNone(estimator)
estimator = cv.detail_HomographyBasedEstimator()
self.assertIsNotNone(estimator)
adjuster = cv.detail_BundleAdjusterReproj()
self.assertIsNotNone(adjuster)
adjuster = cv.detail_BundleAdjusterRay()
self.assertIsNotNone(adjuster)
adjuster = cv.detail_BundleAdjusterAffinePartial()
self.assertIsNotNone(adjuster)
adjuster = cv.detail_NoBundleAdjuster()
self.assertIsNotNone(adjuster)
compensator=cv.detail.ExposureCompensator_createDefault(cv.detail.ExposureCompensator_NO)
self.assertIsNotNone(compensator)
compensator=cv.detail.ExposureCompensator_createDefault(cv.detail.ExposureCompensator_GAIN)
self.assertIsNotNone(compensator)
compensator=cv.detail.ExposureCompensator_createDefault(cv.detail.ExposureCompensator_GAIN_BLOCKS)
self.assertIsNotNone(compensator)
seam_finder = cv.detail.SeamFinder_createDefault(cv.detail.SeamFinder_NO)
self.assertIsNotNone(seam_finder)
seam_finder = cv.detail.SeamFinder_createDefault(cv.detail.SeamFinder_NO)
self.assertIsNotNone(seam_finder)
seam_finder = cv.detail.SeamFinder_createDefault(cv.detail.SeamFinder_VORONOI_SEAM)
self.assertIsNotNone(seam_finder)
seam_finder = cv.detail_GraphCutSeamFinder("COST_COLOR")
self.assertIsNotNone(seam_finder)
seam_finder = cv.detail_GraphCutSeamFinder("COST_COLOR_GRAD")
self.assertIsNotNone(seam_finder)
seam_finder = cv.detail_DpSeamFinder("COLOR")
self.assertIsNotNone(seam_finder)
seam_finder = cv.detail_DpSeamFinder("COLOR_GRAD")
self.assertIsNotNone(seam_finder)
blender = cv.detail.Blender_createDefault(cv.detail.Blender_NO)
self.assertIsNotNone(blender)
blender = cv.detail.Blender_createDefault(cv.detail.Blender_FEATHER)
self.assertIsNotNone(blender)
blender = cv.detail.Blender_createDefault(cv.detail.Blender_MULTI_BAND)
self.assertIsNotNone(blender)
timelapser = cv.detail.Timelapser_createDefault(cv.detail.Timelapser_AS_IS);
self.assertIsNotNone(timelapser)
timelapser = cv.detail.Timelapser_createDefault(cv.detail.Timelapser_CROP);
self.assertIsNotNone(timelapser)
class stitching_compose_panorama_test_no_args(NewOpenCVTests):
def test_simple(self):
img1 = self.get_sample('stitching/a1.png')
img2 = self.get_sample('stitching/a2.png')
stitcher = cv.Stitcher.create(cv.Stitcher_PANORAMA)
stitcher.estimateTransform((img1, img2))
result, _ = stitcher.composePanorama()
assert result == 0
class stitching_compose_panorama_args(NewOpenCVTests):
def test_simple(self):
img1 = self.get_sample('stitching/a1.png')
img2 = self.get_sample('stitching/a2.png')
stitcher = cv.Stitcher.create(cv.Stitcher_PANORAMA)
stitcher.estimateTransform((img1, img2))
result, _ = stitcher.composePanorama((img1, img2))
assert result == 0
class stitching_matches_info_test(NewOpenCVTests):
def test_simple(self):
finder = cv.ORB.create()
img1 = self.get_sample('stitching/a1.png')
img2 = self.get_sample('stitching/a2.png')
img_feat1 = cv.detail.computeImageFeatures2(finder, img1)
img_feat2 = cv.detail.computeImageFeatures2(finder, img2)
matcher = cv.detail.BestOf2NearestMatcher_create()
matches_info = matcher.apply(img_feat1, img_feat2)
self.assertIsNotNone(matches_info.matches)
self.assertIsNotNone(matches_info.inliers_mask)
class stitching_range_matcher_test(NewOpenCVTests):
def test_simple(self):
images = [
self.get_sample('stitching/a1.png'),
self.get_sample('stitching/a2.png'),
self.get_sample('stitching/a3.png')
]
orb = cv.ORB_create()
features = [cv.detail.computeImageFeatures2(orb, img) for img in images]
matcher = cv.detail_BestOf2NearestRangeMatcher(range_width=1)
matches = matcher.apply2(features)
# matches[1] is image 0 and image 1, should have non-zero confidence
self.assertNotEqual(matches[1].confidence, 0)
# matches[2] is image 0 and image 2, should have zero confidence due to range_width=1
self.assertEqual(matches[2].confidence, 0)
class stitching_seam_finder_graph_cuts(NewOpenCVTests):
def test_simple(self):
images = [
self.get_sample('stitching/a1.png'),
self.get_sample('stitching/a2.png'),
self.get_sample('stitching/a3.png')
]
images = [cv.resize(img, [100, 100]) for img in images]
finder = cv.detail_GraphCutSeamFinder('COST_COLOR_GRAD')
masks = [cv.UMat(255 * np.ones((img.shape[0], img.shape[1]), np.uint8)) for img in images]
images_f = [img.astype(np.float32) for img in images]
masks_warped = finder.find(images_f, [(0, 0), (75, 0), (150, 0)], masks)
self.assertIsNotNone(masks_warped)
if __name__ == '__main__':
NewOpenCVTests.bootstrap()